This proposal brings together leading experts in human pluripotent stem cell biology (Thomson), tissue engineering (Murphy), and machine learning (Page) to develop improved human cellular models for predicting developmental neural toxicity. Dramatic progress has been made in the derivation of many of the basic cellular components of the brain from human pluripotent stem cells (ES and iPS cells), but these advances have yet to be applied to predictive toxicology. The major components of the brain are derived from diverse embryological origins, including the neural plate (neurons, oligodendrocytes, and astrocytes), yolk sac myeloid progenitors (microglia), migratory mesodermal angioblasts (endothelial cells), and neural crest (vascular smooth muscle and pericytes). Because of their diverse origins, these components have very different inductive signaling histories. This means that deriving them all at once under the same conditions is not currently possible. For this reason, we will differentiate human pluripotent stem cells to early precursors of the major neural, glial, and vascular components of the cerebral cortex separately, cryopreserve the precursors, and subsequently combine them in 3D hydrogel assemblies to allow increased physiological interactions and maturation. Specifically, we will embed committed precursors for endothelial cells, pericytes, and microglia into hydrogels displaying combinations of peptide motifs that promote capillary network formation. We will then overlay this mesenchymal layer with neural and glial precursors to mimic the normal interactions between the cephalic mesenchyme and the neural epithelium, and promote the formation of the polarized layers of the cerebral cortex. After drug exposure, we will assess temporal changes in gene expression by these cerebral neural- vascular assemblies using highly multiplexed, deep RNA sequencing. Then, using safe drugs and known neural/developmental toxins from the NIH Clinical Collection, the University of Washington Teratogen Information System Database, and the EPA's Toxicity Reference Database as training sets, we will develop machine learning algorithms to predict neural toxicity of blinded drugs known to have failed in late stage animal testing or human clinical trials. This predictive, developmental neural toxicity model will be implemented on liquid handling robots and sequencers in widespread use, and will be readily adaptable to platforms being developed in complementary efforts by DARPA. The developmental potential of human pluripotent stem cells, the modular nature of the tunable hydrogels, and the discriminatory power of machine learning tools also makes the general approaches proposed readily applicable to predictive toxicity models for other tissue types throughout the body.